Add to ORKGWe review our recent investigation of on-line unsupervised learning from high-dimensional structured data. First, on-line competitive learning is studied as a method for the identification of prototype vectors from overlapping clusters of examples. Specifically, we analyse the dynamics of the well-known winner-takes-all or K-means algorithm. As a second standard learning technique, the application of Sanger's rule for principal component analysis is investigated. In both scenarios the necessary process of student specialization may be delayed significantly due to underlying symmetries.
We analyse on-line learning of a linearly separable rule with a simple perceptron. Example inputs ar...
The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making deci...
Winner-Takes-All (WTA) prescriptions for learning vector quantization (LVQ) are studied in the frame...
From the recent analysis of supervised learning by on-line gradient descent in multilayered neural n...
The learning dynamics of an on-line algorithm for principal component analysis is described exactly ...
Various techniques, used to optimize on-line principal component analysis, are investigated by metho...
Abstract. The learning dynamics of an on-line algorithm for principal component analysis is describe...
We present a solvable model of unsupervised competitive learning, which determines prototype vector...
Unsupervised competitive learning classifies patterns based on similarity of their input representat...
An overview is given of prototype-based models in machine learning. In this framework, observations,...
AbstractIn this article, we consider unsupervised learning from the point of view of applying neural...
A model of unsupervised learning is studied, where the environment provides N-dimensional input exam...
On-line learning of a rule given by an N-dimensional Ising perceptron, is considered for the case wh...
Abstract. The areas of On-Line Algorithms and Machine Learning are both concerned with problems of m...
We analyse on-line learning of a linearly separable rule with a simple perceptron. Example inputs ar...
We analyse on-line learning of a linearly separable rule with a simple perceptron. Example inputs ar...
The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making deci...
Winner-Takes-All (WTA) prescriptions for learning vector quantization (LVQ) are studied in the frame...
From the recent analysis of supervised learning by on-line gradient descent in multilayered neural n...
The learning dynamics of an on-line algorithm for principal component analysis is described exactly ...
Various techniques, used to optimize on-line principal component analysis, are investigated by metho...
Abstract. The learning dynamics of an on-line algorithm for principal component analysis is describe...
We present a solvable model of unsupervised competitive learning, which determines prototype vector...
Unsupervised competitive learning classifies patterns based on similarity of their input representat...
An overview is given of prototype-based models in machine learning. In this framework, observations,...
AbstractIn this article, we consider unsupervised learning from the point of view of applying neural...
A model of unsupervised learning is studied, where the environment provides N-dimensional input exam...
On-line learning of a rule given by an N-dimensional Ising perceptron, is considered for the case wh...
Abstract. The areas of On-Line Algorithms and Machine Learning are both concerned with problems of m...
We analyse on-line learning of a linearly separable rule with a simple perceptron. Example inputs ar...
We analyse on-line learning of a linearly separable rule with a simple perceptron. Example inputs ar...
The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making deci...
Winner-Takes-All (WTA) prescriptions for learning vector quantization (LVQ) are studied in the frame...